To deal with the huge volume of information provided by remote sensing satellites, which produce images used for agriculture monitoring, urban planning, deforestation detection and so on, several algorithms for image classification have been proposed in the literature. This article compares two approaches, called Expectation-Maximization (EM) and Self-Organizing Maps (SOM) applied to unsupervised image classification, i.e. data clustering without direct intervention of specialist guidance. Remote sensing images are used to test both algorithms, and results are shown concerning visual quality, matching rate and processing time.
|Title of host publication||SITIS 2008 - Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems|
|Number of pages||7|
|Publication status||Published - 2008|
|Event||4th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2008 - Bali, Indonesia|
Duration: 30 Nov 2008 → 3 Dec 2008
|Conference||4th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2008|
|Period||30/11/08 → 3/12/08|